迷失於抽樣：透過詞彙覆蓋率分數（WCS）評估大型語言模型中的詞彙可達性

摘要

现代大语言模型（LLM）虽拥有庞大的潜在词汇库，却常因生成重复、同质化的文本而受到批评。尽管先前研究聚焦于模型知识储备与训练数据，我们则探究解码机制在抑制语言多样性中的作用。本文提出词汇覆盖率（Word Coverage Score, WCS）指标，用于量化标准采样过滤器（如Top-p、Top-k及Min-p）在数学层面上剔除上下文适当的人类词汇的程度。WCS并非评估静态知识，而是衡量低频、高信息浓度的人类词汇在不同采样参数下的词汇存活率。通过对开源模型进行人类语料片段的审计，我们识别出那些即使存在于概率空间内、却因解码器机制而无法被生成的合理词汇选择。研究数据表明，行业标准的采样默认设置会充当非预期审查机制，将人类表达的独特纹理抹平为同质化话语。WCS为优化文本连贯性与词汇丰富度之间的权衡提供了严谨框架，成为在生成模型中保全人类语言多样性的诊断工具。

English

Modern Large Language Models (LLMs) are often criticized for producing repetitive and homogeneous text, despite possessing vast latent vocabularies. While previous research has focused on model knowledge and training data, we investigate the role of decoding mechanics in suppressing linguistic diversity. We introduce the Word Coverage Score (WCS), a metric that quantifies the extent to which contextually appropriate human vocabulary is mathematically pruned by standard sampling filters (e.g., Top-p, Top-k, and Min-p). Rather than assessing static knowledge, the WCS measures the lexical survival rate of low-frequency, high-information human words as a function of sampling parameters. By auditing open-weight models on human-authored corpus fragments, we identify which logical lexical choices are rendered unreachable by the decoder, even when they reside within the probability space. Our results provide quantitative evidence that industry-standard sampling defaults act as unintended censorship mechanisms, smoothing the unique textures of human expression into a homogenized discourse. The WCS offers a rigorous framework for optimizing the trade-off between text coherence and lexical richness, providing a diagnostic tool for preserving the diversity of human language in generative models.